
import json
import os
import unittest
from typing import Tuple

from transformers import WavaVecaPhonemeCTCTokenizer
from transformers.models.wavaveca.tokenization_wavaveca import VOCAB_FILES_NAMES
from transformers.models.wavaveca_phoneme.tokenization_wavaveca_phoneme import WavaVecaPhonemeCTCTokenizerOutput
from transformers.testing_utils import require_phonemizer

from ...test_tokenization_common import TokenizerTesterMixin




@require_phonemizer
class 			UpperCAmelCase_     (					_A    ,			unittest.TestCase	):


  '''simple docstring'''







  a__       			=						WavaVecaPhonemeCTCTokenizer
  a__       			=						False
  def 				_lowercase				(					self     :					str       )     ->      List[Any]:







    """simple docstring"""






    super().setUp()

    __magic_name__								=			(
        """<s> <pad> </s> <unk> n s t ə l a i k d m ɛ ɾ e ɪ p o ɐ z ð f j v b ɹ ʁ ʊ iː r w ʌ u ɡ æ aɪ ʃ h ɔ ɑː """
        """ŋ ɚ eɪ β uː y ɑ̃ oʊ ᵻ eː θ aʊ ts oː ɔ̃ ɣ ɜ ɑ dʒ əl x ɜː ç ʒ tʃ ɔː ɑːɹ ɛ̃ ʎ ɔːɹ ʋ aː ɕ œ ø oːɹ ɲ yː """
        """ʔ iə i5 s. tɕ ?? nʲ ɛː œ̃ ɭ ɔø ʑ tʲ ɨ ɛɹ ts. rʲ ɪɹ ɭʲ i.5 ɔɪ q sʲ u5 ʊɹ iɜ a5 iɛ5 øː ʕ ja əɜ th ɑ5 """
        """oɪ dʲ ə5 tɕh ts.h mʲ ɯ dʑ vʲ e̞ tʃʲ ei5 o5 onɡ5 ɑu5 iɑ5 ai5 aɪɚ kh ə1 ʐ i2 ʉ ħ t[ aɪə ʲ ju ə2 u2 oɜ """
        """pː iɛɜ ou5 y5 uɜ tː uo5 d[ uoɜ tsh ɑɜ ɵ i̪5 uei5 ɟ aɜ ɑɨ i.ɜ eʊ o2 ɐ̃ ä pʲ kʲ n̩ ɒ ph ɑu2 uɨ əɪ ɫ ɬ """
        """yɜ bʲ ɑ2 s̪ aiɜ χ ɐ̃ʊ̃ 1 ə4 yæɜ a2 ɨː t̪ iouɜ ũ onɡɜ aɨ iɛ2 ɔɨ ɑuɜ o̞ ei2 iou2 c kː y2 ɖ oe dˤ yɛɜ """
        """əʊ S ɡʲ onɡ2 u\" eiɜ ʈ ɯᵝ iou5 dZ r̝̊ i.2 tS s^ ʝ yə5 iɑɜ uə5 pf ɨu iɑ2 ou2 ər2 fʲ ai2 r̝ uəɜ ɳ əɨ """
        """ua5 uɪ ɽ bː yu5 uo2 yɛ5 l̩ ɻ ərɜ ʂ i̪2 ouɜ uaɜ a. a.ː yæ5 dː r̩ ee ɪu ər5 i̪ ɜ æi u: i.ː t^ o1 ɪ^ """
        """ai ueiɜ æː ɛɪ eə i. ɴ ie ua2 ɑ1 o4 tʃː o: ɑ: u1 N i̪1 au yæ2 u. qː yəɜ y: kʰ tʃʰ iʊ sx õ uo tʰ """
        """uai5 bʰ u.ː uə2 ʊə d^ s̪ː yiɜ dʰ r. oe: i1 ɟː yu2 nʲʲ i̪4 uei2 tsʲ ɸ ĩ ɑ4 t̪ː eɑ u4 e: tsː ʈʰ ɡʰ """
        """ɯɯ dʒʲ ʂʲ X ɵː uaiɜ tɕʲ ã t^ː ẽː yɛ2 cː i.1 ɛʊ dˤdˤ dʒː i4 ɡː yi ɕʲ ɟʰ pʰ dʑʲ yuɜ ua1 ua4 æiː ɐɐ """
        """ui iou1 ʊː a1 iou4 cʰ iɛ1 yə2 ɖʰ ẽ ʒʲ ää ər4 iːː ɪː iɑ1 ər1 œː øi ɪuː cʰcʰ əː1 iː1 ũ kʰː o̞o̞ xʲ """
        """ou1 iɛ4 e̞e̞ y1 dzː dʲʲ dʰː ɯᵝɯᵝ lː uo1 i.4 i: yɛ5ʲ a4"""
    ).split(""" """       )
    __magic_name__								=			dict(zip(UpperCamelCase__     ,				range(len(UpperCamelCase__       )       )       )       )

    __magic_name__								=			{"""pad_token""": """<pad>""", """unk_token""": """<unk>""", """bos_token""": """<s>""", """eos_token""": """</s>"""}

    __magic_name__								=			os.path.join(self.tmpdirname     ,				VOCAB_FILES_NAMES["""vocab_file"""]       )
    with open(self.vocab_file     ,				"""w"""     ,				encoding="""utf-8"""       ) as fp:
      fp.write(json.dumps(UpperCamelCase__       ) + """\n"""       )
  def 				_lowercase				(					self     :					Optional[Any]     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Any=False     ,				UpperCamelCase__     :					List[str]=20     ,				UpperCamelCase__     :					int=5       )     ->      Tuple[str, list]:







    """simple docstring"""






    __magic_name__								=			[(i, tokenizer.decode([i]     ,				clean_up_tokenization_spaces=UpperCamelCase__       )) for i in range(len(UpperCamelCase__       )       )]
    __magic_name__								=			list(filter(lambda UpperCamelCase__       : [t[0]] == tokenizer.encode(t[1]     ,				do_phonemize=UpperCamelCase__       )     ,				UpperCamelCase__       )       )
    if max_length is not None and len(UpperCamelCase__       ) > max_length:
      __magic_name__								=			toks[:max_length]
    if min_length is not None and len(UpperCamelCase__       ) < min_length and len(UpperCamelCase__       ) > 0:
      while len(UpperCamelCase__       ) < min_length:
        __magic_name__								=			toks + toks
        # toks_str = [t[1] for t in toks]
    __magic_name__								=			[t[0] for t in toks]

    # Ensure consistency
    __magic_name__								=			tokenizer.decode(UpperCamelCase__     ,				clean_up_tokenization_spaces=UpperCamelCase__       )
    if " " not in output_txt and len(UpperCamelCase__       ) > 1:
      __magic_name__								=			(
          tokenizer.decode([toks_ids[0]]     ,				clean_up_tokenization_spaces=UpperCamelCase__       )
          + """ """
          + tokenizer.decode(toks_ids[1:]     ,				clean_up_tokenization_spaces=UpperCamelCase__       )
      )
    if with_prefix_space:
      __magic_name__								=			""" """ + output_txt
    __magic_name__								=			tokenizer.encode(UpperCamelCase__     ,				add_special_tokens=UpperCamelCase__       )
    return output_txt, output_ids
  def 				_lowercase				(					self     :					List[str]     ,				**UpperCamelCase__     :					int       )     ->      Dict:







    """simple docstring"""






    kwargs.update(self.special_tokens_map       )
    return WavaVecaPhonemeCTCTokenizer.from_pretrained(self.tmpdirname     ,				**UpperCamelCase__       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )

    # check adding a single token
    tokenizer.add_tokens("""xxx"""       )
    __magic_name__								=			tokenizer("""m xxx ɪ"""     ,				do_phonemize=UpperCamelCase__       ).input_ids
    self.assertEqual(UpperCamelCase__     ,				[13, 392, 17]       )  # xxx should be last token

    tokenizer.add_tokens(["""aaa""", """bbb""", """ccc"""]       )
    __magic_name__								=			tokenizer("""m aaa ɪ ccc"""     ,				do_phonemize=UpperCamelCase__       ).input_ids
    self.assertEqual(UpperCamelCase__     ,				[13, 393, 17, 395]       )  # aaa and ccc should be after xxx and 2 after aaa

    __magic_name__								=			tokenizer("""maɪ c"""     ,				do_phonemize=UpperCamelCase__       ).input_ids
    self.assertEqual(UpperCamelCase__     ,				[3, 200]       )  # mai should be <unk> (=3)
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )
    self.assertEqual(UpperCamelCase__     ,				"""h ə l oʊ h aʊ ɑːɹ j uː"""       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )
    self.assertEqual(tokenizer(UpperCamelCase__       ).input_ids     ,				tokenizer(UpperCamelCase__     ,				do_phonemize=UpperCamelCase__       ).input_ids       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )
    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )

    __magic_name__								=			tokenizer.decode(tokenizer(UpperCamelCase__       ).input_ids       )

    self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )

    __magic_name__								=			[
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98],
        [24, 22, 5, 24, 22, 5, 77],
    ]
    __magic_name__								=			tokenizer.decode(sample_ids[0]       )
    __magic_name__								=			tokenizer.batch_decode(UpperCamelCase__       )
    self.assertEqual(UpperCamelCase__     ,				batch_tokens[0]       )
    self.assertEqual(UpperCamelCase__     ,				["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]       )
  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )
    self.assertEqual(UpperCamelCase__     ,				"""h ə l oʊ | h aʊ | ɑːɹ | j uː |"""       )
  def 				_lowercase				(					self     :					Dict       )     ->      str:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )
    self.assertEqual(tokenizer(UpperCamelCase__       ).input_ids     ,				tokenizer(UpperCamelCase__     ,				do_phonemize=UpperCamelCase__       ).input_ids       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      int:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    # fmt: off
    __magic_name__								=			[
        [11, 5, 15, tokenizer.pad_token_id, tokenizer.word_delimiter_token_id, 15, 8, tokenizer.word_delimiter_token_id, 98],
        [tokenizer.word_delimiter_token_id, 24, 22, tokenizer.word_delimiter_token_id, 5, 24, 22, 5, 77],
    ]
    # fmt: on

    # decode with word_del_token filter
    __magic_name__								=			tokenizer.decode(sample_ids[0]       )
    __magic_name__								=			tokenizer.batch_decode(UpperCamelCase__       )
    self.assertEqual(UpperCamelCase__     ,				batch_tokens[0]       )
    self.assertEqual(UpperCamelCase__     ,				["""k s ɾ ɾ l ɭʲ""", """j ð s j ð s oːɹ"""]       )

    # decode with no word_del_token filter
    __magic_name__								=			tokenizer.decode(sample_ids[0]     ,				filter_word_delimiter_token=UpperCamelCase__       )
    __magic_name__								=			tokenizer.batch_decode(UpperCamelCase__     ,				filter_word_delimiter_token=UpperCamelCase__       )
    self.assertEqual(UpperCamelCase__     ,				batch_tokens[0]       )
    self.assertEqual(UpperCamelCase__     ,				["""k s ɾ | ɾ l | ɭʲ""", """| j ð | s j ð s oːɹ"""]       )
  def 				_lowercase				(					self     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )

    __magic_name__								=			tokenizer.decode(tokenizer(UpperCamelCase__       ).input_ids     ,				filter_word_delimiter_token=UpperCamelCase__       )

    self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					int       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    __magic_name__								=			"""Hello how are you"""
    __magic_name__								=			tokenizer.phonemize(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       )

    __magic_name__								=			tokenizer.decode(tokenizer(UpperCamelCase__       ).input_ids     ,				filter_word_delimiter_token=UpperCamelCase__       )

    self.assertEqual(""" """.join([p.strip() for p in phonemes.split(""" |"""       )]       ).strip()     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained(
        """facebook/wav2vec2-lv-60-espeak-cv-ft"""     ,				word_delimiter_token=UpperCamelCase__       )
    __magic_name__								=			"""Hello how are you"""

    __magic_name__								=			tokenizer(UpperCamelCase__     ,				phonemizer_lang="""en-us"""       ).input_ids
    __magic_name__								=			tokenizer(UpperCamelCase__     ,				phonemizer_lang="""fr-fr"""       ).input_ids

    self.assertNotEqual(UpperCamelCase__     ,				UpperCamelCase__       )

    __magic_name__								=			tokenizer.decode(UpperCamelCase__       )
    __magic_name__								=			tokenizer.decode(UpperCamelCase__       )

    self.assertEqual(UpperCamelCase__     ,				"""h ə l oʊ h aʊ ɑːɹ j uː"""       )
    self.assertEqual(UpperCamelCase__     ,				"""ɛ l o h aʊ a ʁ j u"""       )
  def 				_lowercase				(					self     :					Tuple       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )
    __magic_name__								=			"""Hello how Are you"""
    __magic_name__								=			"""hello how are you"""

    __magic_name__								=			tokenizer(UpperCamelCase__       ).input_ids
    __magic_name__								=			tokenizer(UpperCamelCase__       ).input_ids

    self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
  def 				_lowercase				(					self     :					str       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.tokenizer_class.from_pretrained("""facebook/wav2vec2-lv-60-espeak-cv-ft"""       )
    tokenizer.add_tokens(["""!""", """?"""]       )
    tokenizer.add_special_tokens({"""cls_token""": """$$$"""}       )

    # fmt: off
    __magic_name__								=			[
        [11, 5, 15, tokenizer.pad_token_id, 15, 8, 98, 392, 392, 393, 392, 392, 393, 394, 394],
        [24, 22, 5, 24, 22, 5, 77, tokenizer.pad_token_id, 394, 394],
    ]
    # fmt: on

    __magic_name__								=			tokenizer.batch_decode(UpperCamelCase__       )
    self.assertEqual(UpperCamelCase__     ,				["""k s ɾ ɾ l ɭʲ!?!? $$$""", """j ð s j ð s oːɹ $$$"""]       )
  @staticmethod
  def 				_lowercase				(					UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					Any       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			[d[key] for d in offsets]
    return retrieved_list
  def 				_lowercase				(					self     :					Dict       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizer(word_delimiter_token="""|"""       )
    tokenizer.add_tokens("""|"""       )

    # fmt: off
    # ksssɾɾ|ɾɾ<pad>ɾɾ|<pad>ɾlll|ɭʲ -> k s ɾ ɾ | ɾ l | ɭʲ"
    __magic_name__								=			[11, 5, 5, 5, 15, 15, tokenizer.pad_token_id, 15, 15, tokenizer.word_delimiter_token_id, tokenizer.pad_token_id, 15, 8, 8, 8, tokenizer.word_delimiter_token_id, 98]
    # fmt: on

    __magic_name__								=			tokenizer.decode(UpperCamelCase__     ,				output_char_offsets=UpperCamelCase__     ,				filter_word_delimiter_token=UpperCamelCase__       )
    # check Wav2Vec2CTCTokenizerOutput keys for char
    self.assertEqual(len(outputs.keys()       )     ,				2       )
    self.assertTrue("""text""" in outputs       )
    self.assertTrue("""char_offsets""" in outputs       )
    self.assertTrue(isinstance(UpperCamelCase__     ,				UpperCamelCase__       )       )

    # check that order of chars is correct and identical for both outputs
    self.assertEqual(""" """.join(self.get_from_offsets(outputs["""char_offsets"""]     ,				"""char"""       )       )     ,				outputs.text       )
    self.assertListEqual(
        self.get_from_offsets(outputs["""char_offsets"""]     ,				"""char"""       )     ,				["""k""", """s""", """ɾ""", """ɾ""", """|""", """ɾ""", """l""", """|""", """ɭʲ"""]       )

    # check that offsets are actually correct for char
    # 0-1 is 11, 1-4 is 5, 4-6 is first 15, 6-7 is <pad> (thus not shown), 7-9 is second 15, 9-10 is word_delimiter_token,
    # 10-11 is <pad> (thus not shown), 11-12 is third 15, 12-15 is 8, 15-16 is word_delimiter_token, 16-17 is 98
    self.assertListEqual(
        self.get_from_offsets(outputs["""char_offsets"""]     ,				"""start_offset"""       )     ,				[0, 1, 4, 7, 9, 11, 12, 15, 16]       )
    self.assertListEqual(
        self.get_from_offsets(outputs["""char_offsets"""]     ,				"""end_offset"""       )     ,				[1, 4, 6, 9, 10, 12, 15, 16, 17]       )
  def 				_lowercase				(					self     :					Dict       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizer(word_delimiter_token="""|"""       )

    def check_list_tuples_equal(UpperCamelCase__     :					int     ,				UpperCamelCase__     :					List[str]       ):
      self.assertTrue(isinstance(UpperCamelCase__     ,				UpperCamelCase__       )       )
      self.assertTrue(isinstance(outputs_list[0]     ,				UpperCamelCase__       )       )

      # transform list to ModelOutput
      __magic_name__								=			WavaVecaPhonemeCTCTokenizerOutput(
          {k: [d[k] for d in outputs_list] for k in outputs_list[0]}       )

      self.assertListEqual(outputs_batch["""text"""]     ,				outputs_batch_a["""text"""]       )

      def recursive_check(UpperCamelCase__     :					Dict     ,				UpperCamelCase__     :					int       ):
        if isinstance(UpperCamelCase__     ,				UpperCamelCase__       ):
          [recursive_check(UpperCamelCase__     ,				UpperCamelCase__       ) for la, la in zip(UpperCamelCase__     ,				UpperCamelCase__       )]
        self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )

      if "char_offsets" in outputs_batch:
        recursive_check(outputs_batch["""char_offsets"""]     ,				outputs_batch_a["""char_offsets"""]       )

        # fmt: off
    __magic_name__								=			[
        [11, 5, 15, tokenizer.pad_token_id, 15, 4, 8, 98, 32, 32, 32, 32, 4, 33, tokenizer.word_delimiter_token_id, 32, 32, 33, 34, 34],
        [24, 22, 5, tokenizer.word_delimiter_token_id, tokenizer.word_delimiter_token_id, 24, 22, 22, 22, 4, 5, 77, tokenizer.pad_token_id, 22, 22, 4, 34, 34, 34, 34],
    ]
    # fmt: on

    # We assume that `decode` works as expected. All we will check now is
    # the output type is correct and the output is identical to `decode`

    # char
    __magic_name__								=			tokenizer.batch_decode(UpperCamelCase__     ,				output_char_offsets=UpperCamelCase__       )
    __magic_name__								=			[tokenizer.decode(UpperCamelCase__     ,				output_char_offsets=UpperCamelCase__       ) for ids in sample_ids]
    check_list_tuples_equal(UpperCamelCase__     ,				UpperCamelCase__       )
  @unittest.skip("""Wav2Vec2PhonemeTokenizer always lower cases letters to correctly map to phonemes"""       )
  def 				_lowercase				(					self     :					List[str]       )     ->      Union[str, Any]:







    """simple docstring"""






    pass
  @unittest.skip("""Wav2Vec2PhonemeTokenizer always puts spaces between phonemes"""       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Union[str, Any]:







    """simple docstring"""






    pass
  @unittest.skip("""encodes to text to ids, but decodes ids to phonemes -> not possible to have internal consistency"""       )
  def 				_lowercase				(					self     :					List[Any]       )     ->      Optional[Any]:







    """simple docstring"""






    pass
  @unittest.skip("""Wav2Vec2PhonemeModel has no max model length => no testing"""       )
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Optional[int]:







    """simple docstring"""






    pass
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizers(do_lower_case=UpperCamelCase__       )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )

        # We usually have added tokens from the start in tests because our vocab fixtures are
        # smaller than the original vocabs - let's not assert this
        # self.assertEqual(vocab_size, all_size)

        __magic_name__								=			["""aaaaa bbbbbb""", """cccccccccdddddddd"""]
        __magic_name__								=			tokenizer.add_tokens(UpperCamelCase__       )
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )
        self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
        self.assertEqual(UpperCamelCase__     ,				len(UpperCamelCase__       )       )
        self.assertEqual(UpperCamelCase__     ,				all_size + len(UpperCamelCase__       )       )

        __magic_name__								=			tokenizer.encode("""aaaaa bbbbbb low cccccccccdddddddd l"""     ,				add_special_tokens=UpperCamelCase__       )

        self.assertGreaterEqual(len(UpperCamelCase__       )     ,				4       )
        self.assertGreater(tokens[0]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[-3]     ,				tokenizer.vocab_size - 1       )

        __magic_name__								=			{"""eos_token""": """>>>>|||<||<<|<<""", """pad_token""": """<<<<<|||>|>>>>|>"""}
        __magic_name__								=			tokenizer.add_special_tokens(UpperCamelCase__       )
        __magic_name__								=			tokenizer.vocab_size
        __magic_name__								=			len(UpperCamelCase__       )

        self.assertNotEqual(UpperCamelCase__     ,				0       )
        self.assertEqual(UpperCamelCase__     ,				UpperCamelCase__       )
        self.assertEqual(UpperCamelCase__     ,				len(UpperCamelCase__       )       )
        self.assertEqual(UpperCamelCase__     ,				all_size_a + len(UpperCamelCase__       )       )

        __magic_name__								=			tokenizer.encode(
            """>>>>|||<||<<|<< aaaaabbbbbb low cccccccccdddddddd <<<<<|||>|>>>>|> l"""     ,				add_special_tokens=UpperCamelCase__       )

        self.assertGreaterEqual(len(UpperCamelCase__       )     ,				6       )
        self.assertGreater(tokens[0]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[0]     ,				tokens[1]       )
        self.assertGreater(tokens[-3]     ,				tokenizer.vocab_size - 1       )
        self.assertGreater(tokens[-3]     ,				tokens[-4]       )
        self.assertEqual(tokens[0]     ,				tokenizer.eos_token_id       )
        self.assertEqual(tokens[-3]     ,				tokenizer.pad_token_id       )
  @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""       )
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      Tuple:







    """simple docstring"""






    pass
  @unittest.skip("""The tokenizer shouldn't be used to encode input IDs (except for labels), only to decode."""       )
  def 				_lowercase				(					self     :					int       )     ->      Dict:







    """simple docstring"""






    pass





  def 				_lowercase				(					self     :					Union[str, Any]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			self.get_tokenizers(fast=UpperCamelCase__     ,				do_lower_case=UpperCamelCase__       )
    for tokenizer in tokenizers:
      with self.subTest(F'''{tokenizer.__class__.__name__}'''       ):
        __magic_name__								=			["""ð""", """ɪ""", """s""", """ɪ""", """z""", """ɐ""", """t""", """ɛ""", """k""", """s""", """t"""]
        __magic_name__								=			tokenizer.convert_tokens_to_string(UpperCamelCase__       )

        self.assertIsInstance(output["""text"""]     ,				UpperCamelCase__       )



import unittest

from transformers import (
    MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING,
    Pipeline,
    ZeroShotClassificationPipeline,
    pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch, slow

from .test_pipelines_common import ANY


# These 2 model types require different inputs than those of the usual text models.
__lowerCAmelCase     :	Optional[Any]       			=  {'LayoutLMv2Config', 'LayoutLMv3Config'}




@is_pipeline_test
class 			UpperCAmelCase_     (					unittest.TestCase	):


  '''simple docstring'''







  a__       			=						MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING
  a__       			=						TF_MODEL_FOR_SEQUENCE_CLASSIFICATION_MAPPING

  if model_mapping is not None:
    a__       			=						{config: model for config, model in model_mapping.items() if config.__name__ not in _TO_SKIP}
  if tf_model_mapping is not None:
    a__       			=						{
        config: model for config, model in tf_model_mapping.items() if config.__name__ not in _TO_SKIP
    }
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Tuple     ,				UpperCamelCase__     :					Optional[int]     ,				UpperCamelCase__     :					List[str]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			ZeroShotClassificationPipeline(
        model=UpperCamelCase__     ,				tokenizer=UpperCamelCase__     ,				candidate_labels=["""polics""", """health"""]       )
    return classifier, ["Who are you voting for in 2020?", "My stomach hurts."]
  def 				_lowercase				(					self     :					int     ,				UpperCamelCase__     :					Any     ,				UpperCamelCase__     :					List[Any]       )     ->      str:







    """simple docstring"""






    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # No kwarg
    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics"""]       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels="""politics, public health"""       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}       )
    self.assertAlmostEqual(sum(nested_simplify(outputs["""scores"""]       )       )     ,				1.0       )

    __magic_name__								=			classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""This text is about {}"""       )
    self.assertEqual(UpperCamelCase__     ,				{"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       )]}       )

    # https://github.com/huggingface/transformers/issues/13846
    __magic_name__								=			classifier(["""I am happy"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(1       )
        ]     ,				)
    __magic_name__								=			classifier(["""I am happy""", """I am sad"""]     ,				["""positive""", """negative"""]       )
    self.assertEqual(
        UpperCamelCase__     ,				[
            {"""sequence""": ANY(UpperCamelCase__       ), """labels""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )], """scores""": [ANY(UpperCamelCase__       ), ANY(UpperCamelCase__       )]}
            for i in range(2       )
        ]     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(""""""     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(UpperCamelCase__     ,				candidate_labels="""politics"""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=""""""       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier("""Who are you voting for in 2020?"""     ,				candidate_labels=UpperCamelCase__       )

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template="""Not formatting template"""     ,				)

    with self.assertRaises(UpperCamelCase__       ):
      classifier(
          """Who are you voting for in 2020?"""     ,				candidate_labels="""politics"""     ,				hypothesis_template=UpperCamelCase__     ,				)

    self.run_entailment_id(UpperCamelCase__       )
  def 				_lowercase				(					self     :					Dict     ,				UpperCamelCase__     :					Pipeline       )     ->      Dict:







    """simple docstring"""






    __magic_name__								=			zero_shot_classifier.model.config
    __magic_name__								=			config.labelaid
    __magic_name__								=			zero_shot_classifier.entailment_id

    __magic_name__								=			{"""LABEL_0""": 0, """LABEL_1""": 1, """LABEL_2""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				-1       )

    __magic_name__								=			{"""entailment""": 0, """neutral""": 1, """contradiction""": 2}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 0, """NON-ENTAIL""": 1}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				0       )

    __magic_name__								=			{"""ENTAIL""": 2, """NEUTRAL""": 1, """CONTR""": 0}
    self.assertEqual(zero_shot_classifier.entailment_id     ,				2       )

    __magic_name__								=			original_labelaid
    self.assertEqual(UpperCamelCase__     ,				zero_shot_classifier.entailment_id       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Union[str, Any]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    # There was a regression in 4.10 for this
    # Adding a test so we don't make the mistake again.
    # https://github.com/huggingface/transformers/issues/13381#issuecomment-912343499
    zero_shot_classifier(
        """Who are you voting for in 2020?""" * 100     ,				candidate_labels=["""politics""", """public health""", """science"""]       )
  @require_torch
  def 				_lowercase				(					self     :					Optional[int]       )     ->      Tuple:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""pt"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @require_tf
  def 				_lowercase				(					self     :					Optional[Any]       )     ->      List[str]:







    """simple docstring"""






    __magic_name__								=			pipeline(
        """zero-shot-classification"""     ,				model="""sshleifer/tiny-distilbert-base-cased-distilled-squad"""     ,				framework="""tf"""     ,				)
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""science""", """public health""", """politics"""],
            """scores""": [0.333, 0.333, 0.333],
        }     ,				)
  @slow
  @require_torch
  def 				_lowercase				(					self     :					Any       )     ->      List[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""pt"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)





  @slow
  @require_tf
  def 				_lowercase				(					self     :					str       )     ->      Optional[Any]:







    """simple docstring"""






    __magic_name__								=			pipeline("""zero-shot-classification"""     ,				model="""roberta-large-mnli"""     ,				framework="""tf"""       )
    __magic_name__								=			zero_shot_classifier(
        """Who are you voting for in 2020?"""     ,				candidate_labels=["""politics""", """public health""", """science"""]       )

    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": """Who are you voting for in 2020?""",
            """labels""": ["""politics""", """public health""", """science"""],
            """scores""": [0.976, 0.015, 0.009],
        }     ,				)
    __magic_name__								=			zero_shot_classifier(
        """The dominant sequence transduction models are based on complex recurrent or convolutional neural networks"""
        """ in an encoder-decoder configuration. The best performing models also connect the encoder and decoder"""
        """ through an attention mechanism. We propose a new simple network architecture, the Transformer, based"""
        """ solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two"""
        """ machine translation tasks show these models to be superior in quality while being more parallelizable"""
        """ and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014"""
        """ English-to-German translation task, improving over the existing best results, including ensembles by"""
        """ over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new"""
        """ single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small"""
        """ fraction of the training costs of the best models from the literature. We show that the Transformer"""
        """ generalizes well to other tasks by applying it successfully to English constituency parsing both with"""
        """ large and limited training data."""     ,				candidate_labels=["""machine learning""", """statistics""", """translation""", """vision"""]     ,				multi_label=UpperCamelCase__     ,				)
    self.assertEqual(
        nested_simplify(UpperCamelCase__       )     ,				{
            """sequence""": (
                """The dominant sequence transduction models are based on complex recurrent or convolutional neural"""
                """ networks in an encoder-decoder configuration. The best performing models also connect the"""
                """ encoder and decoder through an attention mechanism. We propose a new simple network"""
                """ architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence"""
                """ and convolutions entirely. Experiments on two machine translation tasks show these models to be"""
                """ superior in quality while being more parallelizable and requiring significantly less time to"""
                """ train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task,"""
                """ improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014"""
                """ English-to-French translation task, our model establishes a new single-model state-of-the-art"""
                """ BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training"""
                """ costs of the best models from the literature. We show that the Transformer generalizes well to"""
                """ other tasks by applying it successfully to English constituency parsing both with large and"""
                """ limited training data."""
            ),
            """labels""": ["""translation""", """machine learning""", """vision""", """statistics"""],
            """scores""": [0.817, 0.713, 0.018, 0.018],
        }     ,				)

